Evidence map›Paper›PMID 40872407›Full record

ReviewMicromachines2025

Multidomain Molecular Sensor Devices, Systems, and Algorithms for Improved Physiological Monitoring.

Lianna D Soriano, Shao-Xiang Go, Lunna Li, Natasa Bajalovic, Desmond K Loke

Abstract readReview
In one paragraph

Review in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lianna D SorianoCollege of Letters and Science, University of California, Berkeley, CA 94720, USA.
Shao-Xiang GoDepartment of Science, Mathematics and Technology, and The AI Mega Centre, Singapore University of Technology and Design, Singapore 487372, Singapore.
Lunna LiThomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, UK.
Natasa BajalovicDepartment of Science, Mathematics and Technology, and The AI Mega Centre, Singapore University of Technology and Design, Singapore 487372, Singapore.ORCID 0000-0001-9246-1936
Desmond K LokeDepartment of Science, Mathematics and Technology, and The AI Mega Centre, Singapore University of Technology and Design, Singapore 487372, Singapore.

Funding

Ministry of Education, Singapore MOE-T2EP50220-0022
6 · The paper itself

Abstract

Molecular sensor systems, e.g., implantables and wearables, provide extensive health-related monitoring. Glucose sensor systems have historically prevailed in wearable bioanalysis applications due to their continuous and reliable glucose monitoring, a feat not yet accomplished for other biomarkers. However, the advancement of reagentless detection methodologies may facilitate the creation of molecular sensor systems for multiple analytes. Improving the sensitivity and selectivity of molecular sensor systems is also crucial for biomarker detection under intricate physiological circumstances. The term multidomain molecular sensor systems is utilized to refer, in general, to both biological and chemical sensor systems. This review examines methodologies for enhancing signal amplification, improving selectivity, and facilitating reagentless detection in multidomain molecular sensor devices. The review also analyzes the fundamental components of multidomain molecular sensor systems, including substrate materials, bodily fluids, power, and decision-making units. The review article further investigates how extensive data gathered from multidomain molecular sensor systems, in conjunction with current data processing algorithms, facilitate biomarker detection for precision medicine.

Indexed as

biomarkershealthcare physiological monitoringmachine learningmolecular sensorsprecision medicine

Identifiers

PMID40872407
PMCPMC12388605

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.